Analysis of Variance (ANOVA) is a hypothesis testing procedure that tests whether two or more means are significantly different from each other.
This article describes how to go create an ANOVA Table as shown below. The image below shows the results for a linear regression using Gender, Age, and Coke's perception of weight-conscious to predict the perception of Coke as a feminine brand.
- Familiarity with the Structure and Value Attributes of Variable Sets.
- A data set consisting of at least two variables.
- In the Anything menu select Advanced Analysis > Analysis of Variance > ANOVA.
- In the object inspector go to the Inputs tab.
- In the Outcome dropdown select the variable to be predicted by the predictor variables.
- Select the predictor variable(s) from the Predictor(s) list.
- Specify the type of regression to perform in the Inputs > Type.
- Linear - The linear regression option is most commonly used when the dependent variable is continuous.
- Binary Logit - This is a form of regression analysis that models a binary dependent variable (e.g. yes/no, pass/fail, win/lose).
- Ordered Logit - The Ordered Logit is a form of regression analysis that models a discrete and ordinal dependent variable with more than two outcomes (Net promoter Score, Customer Satisfaction rating, etc.).
- Multinomial Logit - The Multinomial Logit is a form of regression analysis that models a discrete and nominal dependent variable with more than two outcomes (Yes/No/Maybe, Red/Green/Blue, Brand A/Brand B/Brand C, etc.).
- Quasi-Poisson - The Quasi-Poisson Regression is a generalization of the Poisson regression and is used when modeling an overdispersed count variable.
- NBD - The Negative Binomial Distribution (NBD) Regression is a generalization of the Poisson regression, in which the Negative Binomial distribution replaces the Poisson distribution.
- OPTIONAL: Select the desired Missing Data treatment. (See Missing Data Options).
- OPTIONAL: Select the Auxiliary variables Variables to be used when imputing missing values (in addition to all the other variables in the model). Only shown when Missing data is set to Multiple imputation.
- OPTIONAL: Select the desired Output type.
- ANOVA The ANOVA table as shown in the example above.
- Summary The regression coefficients, their standard errors, t-statistics and p-values.
- Detail The R output from the regression fitting.
- OPTIONAL: Select Variable names to display variable names in the output instead of labels.
- OPTIONAL: To compute standard errors that are robust to violations of the assumption of constant variance (i.e., heteroscedasticity) select Robust standard errors. This is only available when Type is set to Linear. See Robust Standard Errors for more information.